1. Field of the Invention
The present invention relates to transmission of force feedback over a long distance, in particular in the context of developing distributed virtual worlds and telepresence systems.
2. Description of the Prior Art
In the same way that digital video coding techniques are based on a knowledge of visual perception and voice recognition techniques are based on a knowledge of hearing, haptic techniques (from the Greek haptos: hand) are based on a knowledge of gestures.
There are two sorts of fine gesture that can be executed by the hand: ballistic gestures, such as moving the hand toward a glass before taking hold of it, and gestures with touch feedback, such as moving the glass toward the mouth after taking hold of it and closing the clamp formed by the thumb and the fingers. The brain is then informed continuously of the force with which the hand is gripping the glass and of the weight of the glass, which depends on the quantity of liquid in it. The brain then reacts by giving the motor instruction to “grasp” the glass sufficiently tightly for it not to be dropped, but not too tightly, so as not to break it or expend energy uselessly.
Ballistic gestures activate the motor reflex arc but they do not activate the touch feedback sensing reflex arc. Force feedback can be a visual representation of the space, but also a gestural map, that is to say a learned or innate mental representation hardwired into the brain and which automatically generates the sequence of motor instructions to the muscles of the shoulder, the arm and the hand to perform the ballistic gesture as a function of a particular mental representation of the space, in particular of the assumed hand-glass distance.
For ballistic gestures, it is sufficient to transmit information on the gesture to the brain with a sampling frequency of 100 Hz. This means that if a sample of the signal is sent every 10 ms, the signal transmitted will contain all the information pertinent to the ballistic gesture.
Gestures with touch feedback simultaneously activate the motor reflex arc and the sensing reflex arc. The brain closes the loop and in humans the complete cycle takes less than 1 ms. The bandwidth of the sensing neurons in the ends of the fingers, i.e. the maximum mechanical signal frequency that the neurons can detect and transmit to the brain, is greater than 500 Hz. To be able to code a fine gesture in a computer, the force feedback system used must itself have a high operating frequency, in accordance with Shannon's theorem a frequency of at least twice the bandwidth of the fingers.
In practice, force feedback systems on a local machine typically operate at a frequency of 1 kHz and in a local closed loop, meaning that feedback is calculated and then applied to their motors and then perceived by the hand every {fraction (1/1000)} second. This avoids the effect known as the “electric toothbrush” effect: the instrument held in the hand must not give the impression of vibrating.
The frequency of 1 kHz results from the following compromise: it must not be too low, if the tactile impression is to be reproduced finely, or too high, if the computer is to have sufficient time to calculate the feedback force that will represent the fine simulation of the gesture executed in the virtual mechanical world.
If it is necessary to transmit via a telecommunication network fine gestures coded by the force feedback system and fine gestures with feedback, the problem is more complicated because of the generally much greater latency introduced by the network itself.
Using the ISDN technology, the latency is 30 ms, using the ADSL technology it is of the order of 200 ms, and on the Internet it can be as much as 6 s or even lead to the message being purely and simply rejected. The ADSL and Internet latency varies because of the asynchronous nature of the networks. The frequency of 1 kHz is therefore much too high to be maintained if the closed loop includes a return trip via the network—the gesture is coded and then transmitted via the network, applied to a remote object, and feedback from the object is in turn coded and sent back via the network.
A ballistic gesture can be transmitted with a time-delay of the order of 10 ms. Sight is a monodirectional sense: the eye is a kind of camera recording a scene and, ignoring a tolerance value, the brain can perceive the precise visual film with a slight time-delay without disturbing the execution of the gesture.
On the other hand, a fine gesture with feedback requires a loop of less than one millisecond for the return trip to make the decision on the intensity of the force to be applied:                sending of the instruction to the muscle via the motor sensing reflex arc,        mechanical action of the hand on the glass,        sensation at the ends of the fingers of touching the glass (increased contact pressure), and        return of information to the brain via the tactile sensing reflex arc to enable the brain to decide to adjust the force applied to the “clamp”.        
A method known as the “wave transform” method for transmitting this kind of fine gesture is nevertheless described by John Wilson and Neville Hogan of MIT in “Algorithms for Network-Based Force Feedback”, Fourth PHANTOM Users Group Workshop (PUG 99). The method simulates the time-delay introduced by the network by means of an artificial viscosity that stabilizes the feedback loop: the greater the time-delay introduced by the network, the more viscous the system.
The “wave transform” method transposes into the force/speed space the theory of passive quadripole networks with pure time-delay that is well known to the skilled person for electrical voltage/current parameters. The theory is used to calculate the incident and reflected electrical waves as a function of the characteristic impedance of the line. Transmission of the electrical signal is optimized if the line is terminated with the same characteristic impedance.
Ohm's law U=Z×I is transposed into the mechanical space by the law F=Viscosity×Speed and the “wave transform” method consists of adapting a virtual pure time-delay line by assigning it a characteristic impedance (in reality a viscosity), which is that of the remote-controlled robot. The signal is transmitted in the form of its Z transform, S(z)=Σ(s(t)×e(2i×π×n×T)) in which T is the fixed time-delay of the network. The greater the time-delay introduced by the network, the greater the artificial viscosity that must be introduced into the line to stabilize the distributed mechanical simulation of the fine gesture in a closed loop in the network.
The gestural sensation is undoubtedly distorted, but transmission of the useful signal is optimized. This method was published following the Fourth Users Group Workshop (PUG99).
The “wave transform” method requires a synchronous network, i.e. a network whose time-delay is fixed and known, for example an ISDN. It is based on the Z representation of sampled discrete signals whose period is equal to the known fixed time-delay of the network.
It is therefore inapplicable to message-based asynchronous Internet, ATM or UMTS type networks, which are characterized by a variable transmission time-delay and by a rejection if the message is lost or takes too long to cross the network.
The problem of the excessively fast timing of force feedback systems is exacerbated in asynchronous networks, for which:                messages can be lost or rejected or fail to arrive if the acknowledgement is delayed for too long (TCP/IP),        messages which reach the correct destination take a variable time to cross the network,        they do not necessarily arrive in the order in which they were sent, and        there is no common clock for the two machines accurate to within one millisecond.        
The invention proposes to remedy the drawbacks of the prior art systems.